Data Intelligence

Data Fragmentation and How to Overcome It

Actian Corporation

February 10, 2022

data-fragmentation

Data-driven companies do everything they can to efficiently collect and exploit data. But if they are not careful, they risk exposing themselves to a major risk: data fragmentation. In this article, we will go over this threat.

What is Data Fragmentation?

Data fragmentation refers to the dispersion of an organization’s data assets.

This is mainly due to the creation of technological silos and the scattering of data. The more data you have from different sources and stored in different spaces, the more likely it is to be scattered. When data is scattered, it is particularly difficult to get a comprehensive view of the available data assets, especially to reconcile them.

To meet the challenges of digital transformation, companies have to gradually evolve their strategy. And because the volume of data that businesses generate is literally exploding, most organizations have opted for private, public, or hybrid clouds. The diversification of information storage naturally has a perverse effect: data siloing. This siloing may prevent companies from having global visibility on information and may lead them to make wrong decisions.

Challenges Related to Data Fragmentation

Fighting against data fragmentation must be a priority for several reasons.

First of all, data fragmentation degrades the project of developing a true data culture in a company.

Secondly, data fragmentation indirectly distorts the knowledge enterprises have on their customers, products, or ecosystems because it limits their field of vision. Moreover, data fragmentation strongly impacts storage costs: keeping large volumes of data that are poorly or not exploited is quite costly.

Finally, data fragmentation exposes companies to another major risk: with the proliferation of data from various sources, fragmented and unstructured data multiplies.

If left unchecked, the management of this data can affect business operations, slow down data processes, or worse, increase the risks associated with sensitive data.

Fragmented data can sometimes escape data governance and security strategies, consequences that also increase exposure to data breaches. But data fragmentation can be avoided.

What are the Key Steps to Avoid Data Fragmentation?

Is your company ready to start the fight against data fragmentation? 

To start, it is essential to have precise knowledge of all the data available in the organization. To do this, you need to map all of your data assets. Then, you will have to rely on data backup, archiving and exploration solutions gathered within a unique platform. These solutions will give you a global view of all your data, wherever it is stored.

Combined with the vision of a Data Architect, you can then put your data in order and at the same time restructure your data storage in the cloud. 

Finally: to combat data fragmentation, you’ll need to ensure continuous vigilance over all your data.

 
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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Analytics

Engineered Decision Intelligence: The Best Way Forward Part 2

Teresa Wingfield

February 3, 2022

decision intelligence

Part 2: You Need Composable Data and Analytics

In my first blog on engineered decision intelligence, I shared information about what this concept means and why you need it, and then I elaborated on the Gartner recommendation for pairing decision intelligence tools with a common data fabric. But there was a second piece of advice from Gartner: You will need composable data and analytics. That’s the subject I’m covering this time around.

What are Composable Data and Analytics?

Composability is all about using components that work together even though they come from a variety of data, analytics, and AI solutions.* By combining components, according to Garner, you can create a flexible, user-friendly, and user-tailored experience. Many types of analytical tools exist, and the purpose and value delivered by each vary greatly. Composability enables you to assemble their results to gain new, powerful insights.

4 Ways a Modern Data Warehouse Can Better Support Composable Data and Analytics

A modern data warehouse should provide a platform that can empower all the different users in the enterprise to analyze anything, anywhere, anytime, using whatever combination of components they want to use. Here are a few “arrangement” tips.

1. Extend the Data Warehouse With Transactional and Edge Data Processing Capabilities

Historically, there was a clear distinction between a transactional database and a data warehouse. A transactional database tracks and processes business transactions. A data warehouse, in contrast, analyzes historical data. However, modern needs for real-time insights have brought these formerly distinct worlds ever closer together, to the point where, today, there is a strong demand for mixed workloads that combine transactional processing and analytics. You see this in a range of use cases, from automated personalized e-commerce offers and real-time quotes for insurance to credit approval and portfolio management, to name just a few.

Likewise, decision makers are looking for ways to act faster using data from their billions of connected mobile and Internet of Things (IoT) devices. Predictive maintenance, real-time inventory management, production efficiency, and service delivery are just a few of the many areas where real-time analytics on IoT data can help a company cut costs and drive additional revenues.

Real-time transactional analytics and artificial intelligence-enabled insights from IoT data are likely to play increasingly important roles in many organizations. What we’re seeing today is just the beginning of benefit streams to come. Realizing greater benefits will depend upon an organization’s ability to deliver varied data to decision intelligence solutions.

2. Bring in Any Data Source, Anytime

The real-time needs of engineered decision intelligence mean that analytic tools can no longer rely solely on historical data for insights. Decision makers still want on-demand access to data from traditional batch processing sources, but they also want the ability to act on current trends and real-time behaviors. This requires seamless orchestration, scheduling, and management of real-time streaming data from systems throughout the organization and the Internet that are continuously generating it.

In a world that is evolving, data must be available for analysis regardless of where it lives. Since most companies have some combination of cloud and on-premises applications, the data warehouse needs to integrate with systems in both environments. It also needs to be able to work with any type of data in the environment. Business decision-makers that can gain insights from the real-time analysis of both semi-structured and unstructured data, for example, may be able to seize opportunities more efficiently and increase the probability that strategic initiatives will be successful.**

3. Take Advantage of the Efficiencies Enabled by Containerization

A containerized approach makes analytics capabilities more composable so that they can be more flexibly combined into applications. However, this is more advantageous if the data warehouse architecture itself supports containers. Support is key to enabling an organization to meet the resource demands associated with artificial intelligence, machine learning, streaming analytics, and other resource-intensive decision intelligence processing. These workloads strain legacy data warehouse architectures.

Container deployment represents a more portable and resource efficient way to virtualize compute infrastructure versus virtualized deployment.  Because containers virtualize the operating system rather than the underlying hardware, applications require fewer virtual machines and operating systems to run them.

4. Accommodate Any Tool

It’s all well and good if a data warehouse offers its own analytical tools—as long as it can easily accommodate any other tool you might want to use. As I mentioned at the start, the purpose and value delivered by different types of analytical tools vary greatly, and different users—including data engineers, data scientists, business analysts, and business users—need different tools. Look for the flexibility to integrate decision intelligence easily with the data warehouse. Or, if you have unique requirements that require you to build custom applications, look at the development tools the platform supports so that you can achieve the composability that a modern analytics environment requires.

Learn More

If you have found this subject interesting, you may want to check out some of these blogs related to the benefits you can derive from broader decision intelligence composability:

  

* Gartner Top 10 Data and Analytics Trends for 2021
** Semi-structured data is information that does not reside in a relational database but that has some organizational properties that make it easier to analyze (such as XML data). Unstructured data either is not organized in a predefined manner or does not have a predefined data model (examples include Word, PDF, and text files, as well as media logs).

This article was co-authored by Lewis Carr.

Lewis Carr co-author of article on Decision Intelligence

Senior strategic vertical industries, horizontal solutions, product marketing, product management, and business development professional focused on Enterprise software, including Data Management and Analytics, Mobile and IoT, and distributed Cloud computing.

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

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Data Intelligence

What is the Difference Between a Data Architect and a Data Engineer?

Actian Corporation

January 31, 2022

data-architect-data-engineer

The growing importance of data in organizations undergoing digital transformation is redefining the roles and missions of data-driven people within the organization. Among these key profiles are the Data Architect and the Data Engineer. For most people, both of these functions are unclear: although their roles can seem quite similar, their purposes and missions are quite different. 

Because enhancing data is a complex task, organizations must work with the right people: specialists who can create a data-driven culture. It is recommended to hire a Data Architect and a Data Engineer within the data department. Although these two key roles overlap and often lead to confusion, they each fulfill different missions. To know whether or not you should hire a Data Architect or a Data Engineer (or both), it is important to understand their scopes of work to create data synergy.

The Wide Range of Skills of a Data Architect

A Data Architect’s main mission is to organize all the data available within the organization. To do so, they must be able to not only identify and map the data but also prioritize it according to its value, volume, and criticality. Researching, identifying, mapping, prioritizing, segmenting data…the work of a Data Architect is complex and these profiles are particularly sought after. And for good reason. Once this inventory of data has been completed, the Data Architect can define a master plan to rationalize the organization of the data.

A Data Architect intervenes in the first phases of a data project and must therefore lay the foundations for exploiting data in a company. As such, they are an essential link in the value chain of your data teams. Their work is then used by data analysts, data scientists, and, ultimately, by all your employees.

What are the Essential Skills of a Data Engineer?

A Data Engineer follows a Data Architect in this vast task of creating the framework for researching and retrieving data. How do they do this? With their ability to understand and decipher the strengths and weaknesses of the organization’s data sources. As a true field player, they are a key to identifying enterprise-wide data assets. Highly qualified, a Data Engineer is an essential part of a data-driven project.

If a Data Architect designs the organization of the data, the Data Engineer ensures its management, the respect of good practices in the processing, the modeling and storage on a daily basis. Within the framework of their missions, a Data Engineer must constantly ensure that all of the processes linked to the exploitation of data in an organization are fluid. In other words, a Data Engineer guarantees the quality and relevance of the data, while using the framework defined by the Data Architect with whom they must act in concert.

Data Architect vs. Data Engineer: Similar…but Above All, Complementary

A Data Architect and a Data Engineer often follow similar training and have comparable skills in IT development and data exploitation. However, a Data Architect, with their experience in database technology, brings a different value to your data project. With more conceptual contributions, a Data Architect needs to rely on the concrete vision of a Data Engineer. The combination of these key profiles will allow you fully exploit enterprise data. Indeed, a Data Architect and a Data Engineer work together to conceptualize, visualize, and build a framework for managing data.

This perfect duo will allow any organization to maximize its data projects success and above all, create conditions for a sustainable, rational and ROI-driven exploitation of your data.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Management

What’s an Edge Data Fabric?

Actian Corporation

January 31, 2022

edge data fabric

What’s an Edge Data Fabric?

A data fabric is a data architecture, management practices, and policies to deliver a set of data services that span all these domains and endpoints. Data fabrics provide that framework. They essentially serve as both the translator and the plumbing for data in all its forms, wherever it sits and wherever it needs to go, regardless of whether the data consumer is a human or machine.

Data fabrics aren’t brand new, but they are suddenly getting a lot of attention in IT these days as companies move to multi-cloud and the edge. That’s because organizations desperately need a framework to manage it – to move it, secure it, prepare it, govern it, and integrate it into IT systems.

Data fabrics got their start back in the mid-2000s when computing started to spread from data centers into the cloud. They became more popular as organizations embraced hybrid clouds, and today data fabrics are helping to reduce complexities involving data streams moving to and from the network’s edge. But the goalposts have moved, the network’s edge is now the IoT, collectively labeled “the edge.”

What’s different is where the data will emanate from and how fluid it will be. In other words, mobile and IoT – the edge – will drive data creation. Further, the processing and analysis will happen at various points from on the device, at the gateways, and across the cloud. Perhaps a better term would be Fluid Distributed Data instead of Big Data?

Regardless, more data ultimately translates to more viable business opportunities – particularly given that this new data is generated at the point of action from humans and machines. To take full advantage of the growing amounts of data available to them, enterprises need a way to manage it more efficiently across platforms, from the edge to the cloud and back. They need to process, store, and optimize different types of data that come from different sources with different levels of cleanliness and validity so they can connect it to internal applications and apply business process logic, increasingly aided by artificial intelligence and machine learning models.

It’s a big challenge. One solution enterprises are pursuing now is the adoption of a data fabric. And, as data volumes continue to grow at the network’s edge, that solution will evolve further into what will more commonly be referred to as an edge data fabric.

How Data Fabric Applies to the Edge

Edge computing provides a unique set of challenges for data being generated and processed outside the network core. The devices themselves operating at the edge are getting more complex. Smart devices like networked PLCs manage solenoids that, in turn, control process flows in a chemical plant, pressure sensors that determine the weight and active RFID tags to determine the location of a cargo container. The vast majority of the processing used to take place in the data center, but that has shifted to the point where a larger portion of the processing takes place in the cloud. In both cases, the processing happens on one side of a gateway. The data center was fixed, not virtual, but the cloud is fluid. If you consider the definition of cloud, you can see why a data fabric would be needed in it. Cloud is about fluidity and removing locality, but, like the data center, it’s about processing data associated with applications. We may not care where the Salesforce cloud or Oracle cloud or any other cloud is actually located but we do care that my data must transit between various clouds and persist in each of them for use in different operations.

Because of all that complexity, organizations have to determine which pieces of the processing are done at which level. There’s an application for each, and for each application there’s a manipulation. And for each manipulation, there’s processing of data and memory management.

The point of a data fabric is to handle all the complexity. Spark, for example, would be a key element of a data fabric in the cloud, as it quickly has become the easiest way to support streaming data between various cloud platforms from different vendors. The edge is quickly becoming a new cloud, leveraging the same cloud technologies and standards in combination with new, edge-specific networks such as 5G and WLAN 6. And, like the core cloud, there are richer, more intelligent applications running on each device, on gateways, and at what would have been the equivalent of data center running in a coat closet on the factory floor, in an airplane, on a cargo ship and so forth. It stands to reason you will need an analogous edge data fabric to the one that is solidifying in the core cloud.

Edge Data Fabric’s Common Elements

To handle the growing number of data requirements edge devices pose, an edge data fabric has to perform several important functions. It has to be able to:

  • Access to many different interfaces: http, mttp, radio networks, manufacturing networks.
  • Run on multiple operating environments: Most importantly POSIX compliant.
  • Work with key protocols and APIs: Including more recent ones with REST API.
  • Provide JDBC/ODBC database connectivity: For legacy applications and a quick and dirty connection between databases.
  • Handle streaming data: Through standards such as Spark and Kafka.

Conclusion

Data fabric is not a single product, platform, or set of services and neither is edge data fabric. Edge data fabric is an extension of data fabric but, given the differences in resources and requirements at the edge, sufficient change to what is necessary to manage edge data is required. In the next blog we’ll discuss why edge data fabric matters and why now.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

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